Indoor localization has, for several years, been a very active subject of research given that there are many potential applications. Amongst the most promising technologies, the approach using inertial measurement seems to be the most mature for the general consumer applications.
The devices for navigation by inertial measurement combine one or more sensors, such as accelerometers, magnetometers and gyrometers, which are generally of the micro-electromechanical type. Such sensors are very often pre-installed in smartphones and touchscreen tablets.
The inertial methods for the navigation of pedestrians are aimed at estimating the relative displacement of an individual starting from a point of departure which can, for example, be input by the user.
The general principle of the processing may be thought of as the combination of a “podometer” and a “compass”: the “podometer” detects the step of the user, which gives approximate information on the distance traveled, whereas the “compass” indicates in which direction the displacement takes place.
FIG. 1 shows a typical architecture of an intertial navigation system, receiving at its input signals Acc(t) produced by an accelerometer, Mag(t) produced by a magnetometer and Gyr(t) produced by a gyrometer (it is not obligatory for all three types of signals to be present). An orientation block OR combines the input signals in order to calculate the orientation RRT(t) of all of the sensors with respect to a reference frame. For this purpose, the accelerometer measures the direction of gravity, the magnetometer measures the direction of the magnetic field (north) and the gyrometer the speeds of rotation of the sensors. A fusion algorithm (typically an extended Kalman filter or EKF) allows the desired matrix which contains the information on direction to be calculated. See for example the document EP 2 402 715.
A “step detector” block, or podometer, PM uses principally the signals from the accelerometer for detecting the impacts caused by each step. It may also use the others signals, but this scenario is more rare. At the output, it produces two signals:                A signal flag(t) equal to “1” at the moment when the step is detected and “0” otherwise;        A signal freq(t) which indicates the frequency of the steps.        
These two signals are subsequently used by a block MLP containing models for length of step as a function of the frequency in order to estimate the distance d(t).
Finally, a last block TRAJ combines the information on distance and on orientation in order to calculate coordinates x(t), y(t).
This example is non-limiting.
The invention relates to a step detector which may be used as a block PM in an inertial navigator of the type in FIG. 1. Such a step detector is also applicable to other applications, such as the estimation of the number of calories consumed during a walk.
Amongst the very numerous techniques for step detection known from the prior art, that described in the document US 2013/0085677 may be mentioned. It uses only the signals ax(t), ay(t), az(t) coming from a three-axis accelerometer; these signals are processed for determining the norm of the acceleration ∥a(t)∥ which is subsequently filtered. The detection of the steps is carried out by thresholding of the signal ∥a(t)∥. Such a detection method is simple to implement and sensitive (low rate of missed detections); however it exhibits a relatively high rate of false alarms, in other words of spurious detections which occur while the person wearing the podometer is not walking. In the aforementioned document US 2013/0085677, this problem is attenuated, but not really solved, by a technique of adaptive filtering of the frequency of the detected steps.